3 citations · 6 across the 13 of their papers we have counts for
13 papers
Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
Xinyu Yang, Tianxing Chen, Honghao Su +38
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical…
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
Yanming Shao, Zanxin Chen, Wenwei Lin +5
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynMan…
EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
Ning Gao, Jinliang Zheng, Xing Gao +22
We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench comprises 26 diverse and challenging ma…
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
Tianshuo Yang, Guanyu Chen, Yutian Chen +8
While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often compromises the profound rea…
PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World
Yunhan Yang, Chunshi Wang, Junliang Ye +7
Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlookin…
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Yibin Liu, Zhixuan Liang, Zanxin Chen +7
Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems l…
Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop
Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
Tianxing Chen, Zanxin Chen, Baijun Chen +23
Simulation-based data synthesis has emerged as a powerful paradigm for advancing real-world robotic manipulation. Yet existing datasets remain insufficient for robust bimanual mani…
RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
Yao Mu, Tianxing Chen, Zanxin Chen +11
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, th…
G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
Tianxing Chen, Yao Mu, Zhixuan Liang +8
Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seam…
Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking
Xi Wang, Tianxing Chen, Qiaojun Yu +7
Articulated object manipulation requires precise object interaction, where the object's axis must be carefully considered. Previous research employed interactive perception for man…
RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)
Yao Mu, Tianxing Chen, Shijia Peng +6
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, th…